In this paper, we present a robotic system to automatically perform defect inspection tasks over free-form specular surfaces, which the image acquisition sub-system is equipped with a 6-DOF robot manipulator to achieve flexible scanning. Given the mesh model of the workpiece, we implement K-means based region segmentation algorithm on the point cloud after preprocessing. Then, we take the smooth regions as input to plan the scanning path. A projection registration method that robustly localizes the object in the robot’s frame is proposed for real-time workpiece localization. According to the optical features of the high-resolution line scan, we design an image processing pipeline to detect defects from the captured images. We report a detailed experimental study to validate the proposed methodology.

We study dynamics of neural activity in brain-inspired neural networks which comprise both low and high layers of information processing. Information propagates from the low layer which includes ldquoperipheral neuronsrdquo (PNs), and the dynamics is controlled by the feedback from the higher layer of ldquocentral neuronsrdquo (CNs). We use the Hodgkin-Huxley type model to describe spike generation properties of neural elements. Synaptic connections are of excitatory and inhibitory type and some of them have fixed connection strengths and some are adjustable according to Hebbian type learning rule. The regime of partial synchronization between spiking activity of the CNs and PNs has been found. It is shown that PNs with higher firing rates are selected preferentially by the central neurons. In the case of local connections between PNs, we have found that local excitatory connections facilitate synchronization; while local inhibitory connections help distinguishing two groups of PNs with similar intrinsic frequencies. We hypothesize that the regime of partial synchronization can be used to simulate neural mechanisms of perception and attention. In particular, sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model which deals with a real image in the frequency domain.